Chatbot

About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.

Review of integrated applications with AIML based chatbot

Abstract: Artificial Intelligence Markup Language (AIML) is derived from Extensible Markup Language (XML) which is used to build up conversational agent (chatbot) artificially. There are developed a lot of works to make conversational agent. But low cost, configuration and availability make possible to use it in various applications. In this paper, we give a brief review of some applications which are used AIML chatbot for their conversational service. These applications are related to cultural heritage, e-learning, e-government, web base model, dialog model, semantic analysis framework, interaction framework, humorist expert, network management, adaptive modular architecture as well. In this case, they are not only providing useful services but also interact with customers and give solution of their problems through AIML chatbot instead of human beings. So, this is popular day by day with entrepreneur and users to provide efficient service.

Commercial Chatbot: Performance Evaluation, Usability Metrics and Quality Standards of Embodied Conversational Agents

Abstract: The aim of this paper is to explore commercial applications of chatbots, as well as to propose several measurement metrics to evaluate performance, usability and overall quality of an embodied conversational agent. On the basis of these metrics we examine existing Polish-speaking commercial chatbots that a) work in the B2C sector, b) reach the widest possible range of users, and c) are presumably the most advanced commercial deployments of their creators. We analyse various aspects of functioning of each embodied conversational agent: visual look, form of implementation on the website, speech synthesis unit, built-in knowledge base (with general and specialized information), presentation of knowledge and additional functionalities, conversational abilities and context sensitiveness, personality traits, personalization options, emergency responses in unexpected situations, possibility of rating chatbot and the website by the user. Our study reveals the current condition of Polish market of commercial virtual assistants and emphasizes the importance of a multidimensional evaluation of any commercial chatbot deployment.

Chatbot for Disease Prediction and Treatment Recommendation using Machine Learning

Abstract: Hospitals are the most widely used means by which a sick person gets medical check-ups, disease diagnosis and treatment recommendation. This has been a practice by almost all the people over the world. People consider it as the most reliable means to check their health status. The proposed system is to create an alternative to this conventional method of visiting a hospital and making an appointment with a doctor to get diagnosis. This research intends to apply the concepts of natural language processing and machine learning to create a chatbot application. People can interact with the chatbot just like they do with another human and through a series of queries, chatbot will identify the symptoms of the user and thereby, predicts the disease and recommends treatment. This system can be of great use to people in conducting daily check-ups, makes people aware of their health status and encourages people to make proper measures to remain healthy. According to this research, such a system is not widely used and people are less aware of it. Executing this proposed framework can help people avoid the time-consuming method of visiting hospitals by using this free of cost application, wherever they are.

A comparison between Alice and Elizabeth chatbot systems

Abstract: This study examines two chatter bots systems called ALICE and Elizabeth, which are adapted from ELIZA program. Joseph Weizenbaum implemented ELIZA in 1966 and it was originally designed to emulate a psychotherapist. This report also provides an introduction to the analysis of ALICE and Elizabeth focusing in the knowledge representation and pattern matching algorithms for each one of them. The report then illustrates the main differences between them and concludes that it will be easier to build machine learning for ALICE because of its simple pattern matching techniques than building one for Elizabeth since it depends on rules.